The early simulators could best be described as "eye - hand" coordination devices used primarily
to teach and reinforce rule based responses. Since then, the goal has been to create more "real world-like"
simulations. We are moving toward being able to create cost-effective virtual environments (VE), which
can be used for everything from military training applications to industrial simulation. VE gives us the
capability for conducting complete mission rehearsals, enabling the use of a wide variety of instructional
strategies. On the other side of the spectrum, the entertainment industry is rushing ahead to give consumers
what they want, a more engaging environment within which to reap enjoyment. It is likely that low-cost VE
will be commonplace in the near future. Anticipated widespread use of VE deems it necessary to begin
addressing the limitations and capabilities of both today's and tomorrow's technologies for the target
populations.

While VE training and performance research marches on, studies addressing performance of
populations such as older adults is lagging. This in spite of the elderly being the largest growing segment
of the population in the United States. Currently there are 23.5 million individuals over the age of 65. This
group has nearly doubled in size since the early 1900's. The "85 and over" group has increased in size nearly
17 times since the early 1900's. This "85 and over" group is the segment of the elderly population that is
responsible for the rapid growth in size of the elderly population in the United States. With the advent of
recent advances in medical technologies, it is not unusual for an individual to reach the age of 85 in the U.S.
today. It is estimated that by the year 2025, approximately 20% of the U.S. population will be 65 years of
age or older ( Harbin, 1991). People are living much longer, and retiring at an older age than before.
Moreover, with the uncertain future of the social security system, older persons may be forced to continue
working past the age of 65. If VE technology becomes a commonplace medium for training and a number
of other life-related functions more older adults will find themselves being required to use it. However, little
is known about how age-related differences may interact with VE characteristics.

The effectiveness of VE for training or other applications has been attributed largely to the level of
fidelity of the VE system. Regian,
Shebilske, and
Monk ( 1993) maintain that training transfer requires both
preservation of the visual spatial characteristics of real world and that the interface preserves the link
between motor actions and the effects in VE. User capabilities and limitations are important factors to
consider in the interface. User characteristics can have a bearing the system's overall effectiveness. For
example, a person who has a greater than average difficulty resolving images under lower levels of
luminance may not be able to resolve images in a low luminance HMDS, even though the physical fidelity
of that device may be suitable for most other persons for performing the task. According to Bjorn, Kaczmarek, and
Lotens ( 1995), it is important to have a thorough understanding of the capabilities of the

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